Background:
Why is the Phillipines important to China?
The Philippines is situated in the South China Sea and is part of
the broader Asia-Pacific region, which has become a focal point for
global trade and geopolitics. Its location near major shipping routes
makes it strategically important for China
China and the Philippines have overlapping territorial claims in the
South China Sea, particularly around the Spratly Islands and Scarborough
Shoal. China’s assertiveness in the region has led to increased tensions
between the two countries. Securing these territories is important for
China’s strategic interests, as well as for potential resources, such as
oil and gas reserves, in the disputed areas.

Additionally, China has been investing in the Philippines’
infrastructure development, telecommunications, and other sectors as
part of its broader Belt and Road Initiative (BRI). These investments
can help strengthen China’s influence in the region and is a way China
inflitrates host nations economic infrastructure.
Goal:
This project is concentrated on the survey data collected in the
Phillipines and getting a better understanding on the particpants
overall perception of China.
library(dplyr)
library(ggplot2)
library(plotly)
Now I want to visualize the responses with some interactivity, so
ploty graph seemed appropiate.
# Create a Plotly bar chart
plot_ly( x = freq_df$value, y = freq_df$count, type = "bar") %>%
layout(
title = "Overall Perception of China",
xaxis = list(title = "Response"),
yaxis = list(title = "Count")
)
After exploring these results, I noticed that the majority of survey
particpants had a perception of “Neutrality” but oddly enough there is a
good portion of particpants that share a perception of “Supportive”
compared to “Opposed”.
After comprehending the responses as a whole, I wanted to see if a
trend existed when the surveys were isolated by their respective
“Campaign Name.” There was 6 different campaigns that conducted this
survey with a number of participants ranging from 15 - 1,500.
# Group by 'campaign_name'
grouped_data <- Perception_of_China %>%
group_by(campaign_name) %>%
count(what_is_your_overall_perception_of_china)
# Create bar plot with facet wrap
ggplot(grouped_data, aes(x = what_is_your_overall_perception_of_china, y = n, fill = what_is_your_overall_perception_of_china)) +
geom_bar(stat = "identity", position = "dodge") +
facet_wrap(~ campaign_name, scales = "free_y", ncol = 3) +
labs(x = "Overall Perception of China", y = "Count") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
strip.text = element_text(size = 04, face = "bold", color = "black", lineheight = 0.2))

While understanding the above and having some knowledge about the
current climate between the Phillipines and China, I wanted to
investigate if there was a trend between the response and location of
the survey.
# Load required libraries
library(sf)
library(leaflet)
#Subset the latitude and longitude coordinates from the survey data
location_data <- Perception_of_China %>% select(user_id,lat, lon)
#Remove NA Values with missing lat or long values
location_data <- location_data %>%
filter(!is.na(lat) & !is.na(lon))
The majority of the survey responses are in the Phillipines but
there are some outliers in other locations such as the US, Middle East,
Europe, Australia, and a few others.
#Create leaflet map based off user ID
leaflet(location_data) %>%
addProviderTiles(providers$OpenStreetMap) %>%
addCircleMarkers(
lat = ~lat,
lng = ~lon,
stroke = FALSE,
fillOpacity = 0.8,
label = ~as.character(user_id)
)
Now lets dissect perception response and geolocation.
# Define a color function to set different colors for different perception levels
color_perception <- function(perception) {
case_when(
perception == "neutral" ~ "gray",
perception == "oppopsed" ~ "orange",
perception == "supportive" ~ "lightgreen",
perception == "very_oppopsed" ~ "darkred",
perception == "very_supportive" ~ "skyblue"
)
}
# Create the leaflet map
leaflet_map <- leaflet(Perception_of_China) %>%
addTiles() %>%
addCircleMarkers(
~lon,
~lat,
radius = 8,
color = ~color_perception(what_is_your_overall_perception_of_china),
stroke = FALSE,
fillOpacity = 0.8,
label = ~paste("User:", user_id, "<br>Perception of China:", what_is_your_overall_perception_of_china)
) %>%
addLegend(
position = "bottomright", # Set legend position
colors = c("grey", "orange", "lightgreen", "darkred", "skyblue"), # Set the colors used in the legend
labels = c("neutral", "oppopsed", "supportive", "very_oppopsed", "very_supportive"), # Set the labels for the legend
title = "Perception of China", # Set the title for the legend
opacity = 1 # Set the opacity of the legend
)
# Display the map
leaflet_map
Discussion:
This analysis investigated the overall perception of China among
survey participants in the Phillipines. The dataset used in this study
contains various variables, such as the participants’ user ID, location
coordinates, and their overall perception of China, among others. The
perception levels were categorized as neutral, opposed, supportive, very
opposed, and very supportive.
The overall trend was that there was a neutral overall perception of
China. Additionally there was a larger supportive overall perception
compared to opposed.